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/* -*- mode: c++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
/*
Copyright (C) 2006 Ferdinando Ametrano
Copyright (C) 2006 Mark Joshi
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<https://www.quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
#include <ql/models/marketmodels/evolvers/lognormalfwdrateeuler.hpp>
#include <ql/models/marketmodels/marketmodel.hpp>
#include <ql/models/marketmodels/evolutiondescription.hpp>
#include <ql/models/marketmodels/browniangenerator.hpp>
#include <ql/models/marketmodels/driftcomputation/lmmdriftcalculator.hpp>
namespace QuantLib {
LogNormalFwdRateEuler::LogNormalFwdRateEuler(
const ext::shared_ptr<MarketModel>& marketModel,
const BrownianGeneratorFactory& factory,
const std::vector<Size>& numeraires,
Size initialStep)
: marketModel_(marketModel),
numeraires_(numeraires),
initialStep_(initialStep),
numberOfRates_(marketModel->numberOfRates()),
numberOfFactors_(marketModel_->numberOfFactors()),
curveState_(marketModel->evolution().rateTimes()),
forwards_(marketModel->initialRates()),
displacements_(marketModel->displacements()),
logForwards_(numberOfRates_), initialLogForwards_(numberOfRates_),
drifts1_(numberOfRates_), initialDrifts_(numberOfRates_),
brownians_(numberOfFactors_), correlatedBrownians_(numberOfRates_),
alive_(marketModel->evolution().firstAliveRate())
{
checkCompatibility(marketModel->evolution(), numeraires);
Size steps = marketModel->evolution().numberOfSteps();
generator_ = factory.create(numberOfFactors_, steps-initialStep_);
currentStep_ = initialStep_;
calculators_.reserve(steps);
fixedDrifts_.reserve(steps);
for (Size j=0; j<steps; ++j) {
const Matrix& A = marketModel_->pseudoRoot(j);
calculators_.emplace_back(A, displacements_, marketModel->evolution().rateTaus(),
numeraires[j], alive_[j]);
std::vector<Real> fixed(numberOfRates_);
for (Size k=0; k<numberOfRates_; ++k) {
Real variance =
std::inner_product(A.row_begin(k), A.row_end(k),
A.row_begin(k), Real(0.0));
fixed[k] = -0.5*variance;
}
fixedDrifts_.push_back(fixed);
}
setForwards(marketModel_->initialRates());
}
const std::vector<Size>& LogNormalFwdRateEuler::numeraires() const {
return numeraires_;
}
void LogNormalFwdRateEuler::setForwards(const std::vector<Real>& forwards)
{
QL_REQUIRE(forwards.size()==numberOfRates_,
"mismatch between forwards and rateTimes");
for (Size i=0; i<numberOfRates_; ++i)
initialLogForwards_[i] = std::log(forwards[i] +
displacements_[i]);
calculators_[initialStep_].compute(forwards, initialDrifts_);
}
void LogNormalFwdRateEuler::setInitialState(const CurveState& cs) {
setForwards(cs.forwardRates());
}
Real LogNormalFwdRateEuler::startNewPath() {
currentStep_ = initialStep_;
std::copy(initialLogForwards_.begin(), initialLogForwards_.end(),
logForwards_.begin());
return generator_->nextPath();
}
Real LogNormalFwdRateEuler::advanceStep()
{
// we're going from T1 to T2
// a) compute drifts D1 at T1;
if (currentStep_ > initialStep_) {
calculators_[currentStep_].compute(forwards_, drifts1_);
} else {
std::copy(initialDrifts_.begin(), initialDrifts_.end(),
drifts1_.begin());
}
// b) evolve forwards up to T2 using D1;
Real weight = generator_->nextStep(brownians_);
const Matrix& A = marketModel_->pseudoRoot(currentStep_);
const std::vector<Real>& fixedDrift = fixedDrifts_[currentStep_];
Size alive = alive_[currentStep_];
for (Size i=alive; i<numberOfRates_; i++) {
logForwards_[i] += drifts1_[i] + fixedDrift[i];
logForwards_[i] +=
std::inner_product(A.row_begin(i), A.row_end(i),
brownians_.begin(), Real(0.0));
forwards_[i] = std::exp(logForwards_[i]) - displacements_[i];
}
// same as PC evolver with two steps dropped
// c) update curve state
curveState_.setOnForwardRates(forwards_);
++currentStep_;
return weight;
}
Size LogNormalFwdRateEuler::currentStep() const {
return currentStep_;
}
const CurveState& LogNormalFwdRateEuler::currentState() const {
return curveState_;
}
}
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